Treatment resistant depression incidence estimates from studies of health insurance databases depend strongly on the details of the operating definition

Daniel Fife, Jenna Reps, M Soledad Cepeda, Paul Stang, Margaret Blacketer, Jaskaran Singh, Daniel Fife, Jenna Reps, M Soledad Cepeda, Paul Stang, Margaret Blacketer, Jaskaran Singh

Abstract

Background: Health services databases provide population-based data that have been used to describe the epidemiology and costs of treatment resistant depression (TRD). This retrospective cohort study estimated TRD incidence and, via sensitivity analyses, assessed the variation of TRD incidence within the range of implementation choices.

Methods: In three US databases widely used for observational studies, we defined TRD as failure of two medications as evidenced by their replacement or supplementation by other medications, and set maximum durations (caps) for how long a medication regimen could remain in use and still be eligible to fail.

Results: TRD incidence estimates varied approximately 2-fold between the two databases (CCAE, Medicaid) that described socioeconomically different non-elderly populations; for a given cap varied 2-fold to 4-fold within each database across the other implementation choices; and if the cap was also allowed to vary, varied 6-fold or 7-fold within each database.

Limitations: The main limitations were typical of studies from health services databases and included the lack of complete -rather than recent - medical histories, the limited amount of clinical information, and the assumption that medication dispensed was consumed as directed.

Conclusion: In retrospective cohort studies from health services databases, TRD incidence estimates vary widely depending on the implementation choices. Unless a firm basis for narrowing the range of these choices can be found, or a different analytic approach not dependent on such choices is adopted, TRD incidence and prevalence estimates from such databases will be difficult to compare or interpret.

Keywords: Clinical psychology; Epidemiology; Evidence-based medicine; Health sciences; Psychiatry.

References

    1. Bromet E. Cross-national epidemiology of DSM-IV major depressive episode. BMC Med. 2011;9:90.
    1. Kessler R.C., Bromet E.J. The epidemiology of depression across cultures. Annu. Rev. Publ. Health. 2013;34:119–138.
    1. Evidence-Based Practice Center . 2017. Treatment-resistant Depression: a Narrative and Systematic Review of Definitions and Methods in Clinical Research Studies (Technology Assessment Protocol)
    1. European Medicines Agency . 2013. Guideline on Clinical Investigation of Medicinal Products in the Treatment of Depression.
    1. Gaynes B.N. What did STAR*D teach us? Results from a large-scale, practical, clinical trial for patients with depression. Psychiatr. Serv. 2009;60(11):1439–1445.
    1. Rush A.J. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am. J. Psychiatr. 2006;163(11):1905–1917.
    1. Mrazek D.A. A review of the clinical, economic, and societal burden of treatment-resistant depression: 1996-2013. Psychiatr. Serv. 2014;65(8):977–987.
    1. Olchanski N. The economic burden of treatment-resistant depression. Clin. Ther. 2013;35(4):512–522.
    1. Nemeroff C.B. Prevalence and management of treatment-resistant depression. J. Clin. Psychiatr. 2007;68(Suppl 8):17–25.
    1. Kubitz N. Characterization of treatment resistant depression episodes in a cohort of patients from a US commercial claims database. PLoS One. 2013;8(10)
    1. Fife D. Epidemiology of pharmaceutically treated depression and treatment resistant depression in Taiwan. Psychiatr. Res. 2017;252:277–283.
    1. Ivanova J.I. Direct and indirect costs of employees with treatment-resistant and non-treatment-resistant major depressive disorder. Curr. Med. Res. Opin. 2010;26(10):2475–2484.
    1. Overhage J.M. Validation of a common data model for active safety surveillance research. J. Am. Med. Inform. Assoc. 2012;19(1):54–60.
    1. Voss E.A. Feasibility and utility of applications of the common data model to multiple, disparate observational health databases. J. Am. Med. Inform. Assoc. 2015;22(3):553–564.
    1. Fiest K.M. Systematic review and assessment of validated case definitions for depression in administrative data. BMC Psychiatr. 2014;14:289.
    1. Hwang S. Concordance between clinical diagnosis and Medicare claims of depression among older primary care patients. Am. J. Geriatr. Psychiatr. 2015;23(7):726–734.
    1. Noyes K. Medicare beneficiaries with depression: comparing diagnoses in claims data with the results of screening. Psychiatr. Serv. 2011;62(10):1159–1166.
    1. Rost K. The deliberate misdiagnosis of major depression in primary care. Arch. Fam. Med. 1994;3(4):333–337.
    1. Cepeda M.S. Finding treatment-resistant depression in real-world data: how a data-driven approach compares with expert-based heuristics. Depress. Anxiety. 2018;35(3):220–228.

Source: PubMed

Patrocinadores e Colaboradores

Condições médicas

Intervenções de drogas

3
Se inscrever